{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"a403cbfc-5ab6-4009-833a-3a9a90e4f43c","company_id":"a0000000-0000-0000-0000-000000000001","title":"Data Engineer, Billing \u0026 Payments","slug":"data-engineer-billing-payments-d1c26c11","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role As a Data Engineer supporting Billing \u0026 Payments Engineering, you'll join the Finance Data Engineering team and build the foundation for accurate, scalable billing and revenue data across Anthropic. You'll work closely with Billing Engineering, Finance and Accounting, as well as Product and other partners, to turn billing and payments data into reliable metrics, reporting and insights. You'll make sure teams have trustworthy data that scales with our growth, and you'll lead your own projects to enable self-serve insights that help teams make data-driven decisions.\n Responsibilities: • Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements • Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments data into canonical datasets • Own revenue data accuracy and reconciliation, making sure billing, usage and revenue data tie out across systems • Define success metrics for billing systems, and build the data models and monitoring to track them • Set high data integrity standards and SLAs for timely, accurate delivery of billing and revenue data • Share on-call and data operations responsibilities for Finance Data Engineering pipelines • Build dashboards and self-serve data products that track core metrics and deliver insights across the company • Champion best practices for dbt, data modeling and SQL • Influence the Billing Engineering roadmap from a data systems perspective • Become an expert in our data models and data architecture\n You may be a good fit if you have: • 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar role • Expertise in SQL and Python, and in building multi-step data pipelines with tools like dbt • Experience with workflow orchestration tools like Airflow, and version control with GitHub • Familiarity with billing concepts such as invoicing, payments, credits, refunds and subscriptions, and experience working with data from billing systems • Experience building dashboards and reporting in tools like Hex for multiple cross-functional teams • A bias for action and urgency, without letting perfect be the enemy of the effective • A full-stack mindset: you do what it takes to solve a problem end to end, even when it goes beyond the original scope • Comfort with ambiguity, and the initiative to create clarity and keep work moving • A passion for Anthropic's mission of building helpful, honest and harmless AI\n Strong candidates may also have: • Experience partnering with billing, finance or accounting teams on revenue metrics and reconciliation • Experience building an analytics or data engineering function from an early stage\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $405,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. T","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","data-pipeline","alignment","data-engineering","data-science"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5450099008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T19:03:03Z","expires_at":"2026-11-13T16:54:55.362957Z","created_at":"2026-10-14T15:17:21.640971Z","updated_at":"2026-10-14T16:54:55.365861Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a403cbfc-5ab6-4009-833a-3a9a90e4f43c"},{"id":"55139ac1-2db5-471c-b6aa-dbc4dc6745af","company_id":"a0000000-0000-0000-0000-000000000001","title":"Data Engineer, Product","slug":"data-engineer-product-96da0802","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role As a Data Engineer on the Data Science \u0026 Analytics team, you'll build the foundation that lets analytics scale across Anthropic. You'll partner with Engineering, Product and other teams to turn raw data into reliable metrics, reporting and insights, and you'll make sure teams have accurate metrics for our consumer products from idea to launch. You'll also lead your own projects that make self-serve insights possible, so teams can make data-driven decisions.\n Responsibilities \n \n \n Understand, and where possible anticipate, the data needs of partner teams, and translate them into data models, reporting and technical requirements\n \n Define, build and manage key dbt pipelines that turn raw logs into canonical datasets\n \n Set data integrity standards and SLAs so data is delivered on time and accurately\n \n Build reliable dashboards that track core metrics and share insights across the company\n \n Build foundational data products, dashboards and tools that let self-serve analytics scale\n \n Partner with stakeholders to define and materialize metrics and analysis for new and evolving consumer products\n \n Shape Product teams' roadmaps from a data systems perspective\n \n Become an expert in our data models and data architecture\n \n You may be a good fit if you have \n \n \n Significant experience as a Data Engineer or in a similar Data Science \u0026 Analytics role, ideally partnering with Product leads to build and report on company-wide metrics\n \n A passion for Anthropic's mission of building helpful, honest and harmless AI\n \n Expertise building multi-step ETL jobs with tools like dbt, plus experience with workflow tools like Airflow and version control through GitHub\n \n Expertise in SQL and Python for turning data into accurate, clean data models\n \n Experience building reporting and dashboards in tools like Hex that serve multiple cross-functional teams\n \n A bias for action, and a sense of when \"good enough\" beats perfect\n \n An end-to-end mindset: you take ownership of solving a problem fully, even when that means picking up work beyond your usual scope\n \n Comfort with ambiguity, and a habit of creating clarity and forward progress\n \n Experience using AI to scale your own productivity and your team's without lowering the quality of the work\n \n Strong candidates may also have \n \n \n Experience building a data engineering (or similar) function from the ground up in an early-stage or fast-growing environment\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $405,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, ","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","alignment","data-science","data-engineering"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5448481008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T17:00:40Z","expires_at":"2026-11-13T16:54:55.368278Z","created_at":"2026-10-14T15:17:21.646192Z","updated_at":"2026-10-14T16:54:55.371153Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/55139ac1-2db5-471c-b6aa-dbc4dc6745af"},{"id":"4708c050-c2b9-43f4-863a-aabad3dc6845","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"Senior Data Engineer, Product","slug":"senior-data-engineer-product-1e370dcb","description":"It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.\n The Role \n As a Data Engineer, you will handle everything from data architecture and modeling to data pipeline tooling and dashboarding. You will enable a range of other engineering and business teams to make impactful business decisions by laying the foundation of our large and unique datasets that span across multiple products. This role will support the product data engineering team.\n The Product Data Engineering Team at Block builds and maintains the curated data foundation that powers all of Block's product lines — Cash App, Square, Afterpay, and Tidal. We transform raw event streams, transactional systems, and operational data into governed, tested, and reliable analytical assets across domains including banking, lending, commerce, investing, checkout, and AI assistants. We partner with Product, Engineering, Data Science, Machine Learning, and Applied AI teams to deliver the metrics, experimentation frameworks, and ML-ready datasets that enable Block's products to grow, improve, and serve customers at scale.\n  \n You Will \n \n Lead the creation and optimization of data models across eventing, customer-level, and process-level data, leveraging AI-assisted development tools to accelerate delivery.\n Take shared or independent technical ownership of critical data pipelines across Block — including foundational customer datasets, money transfer and movement pipelines, and the data infrastructure powering Block's experimentation platform.\n Build monitoring to assess data quality and lineage and further, build AI agents for false positive reduction and automated resolution. \n Participate in on-call rotation, monitor daily execution, diagnose and log issues, and fix business critical pipelines to ensure SLAs are met with internal stakeholders. Automate this process through AI agents to focus human effort on highest priority tasks.\n Work with non-technical partners and product teams to understand their needs, translate business requirements into applicable data requirements, and develop automated end-to-end solutions.\n \n  \n You Have \n \n A minimum of 10 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or equivalent experience.\n High proficiency in SQL, Python, and Dbt\n Experience designing medium-to-large data engineering solutions and responsible for the entire lifecycle of projects including scoping, design, development, testing, deployment, and documentation\n Experience with ETL scheduling technologies with dependency checking, such as Airflow or Prefect, as well as \n Experience in schema design and dimensional data modeling\n Experience with setting up data quality and data lineage monitoring\n Experience with data driven decisions through AI and agentic automation\n \n  \n Technologies We Use and Teach \n \n Snowflake\n Databricks\n Dbt\n Github\n Airflow\n Prefect\n Omni\n Terraform\n \n  \n We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page .\n While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.\n  \n Block takes a market-","salary_min":168300,"salary_max":252500,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","payments","cloud","data-pipeline","data-science","data-engineering"],"apply_url":"http://block.xyz/careers/jobs/5442647008?gh_jid=5442647008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T21:28:47Z","expires_at":"2026-11-13T16:58:11.230731Z","created_at":"2026-10-14T15:21:51.255254Z","updated_at":"2026-10-14T16:58:11.233468Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4708c050-c2b9-43f4-863a-aabad3dc6845"},{"id":"208f6a9b-9968-46f0-aa68-f972d885cce0","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-fc07da4b","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198500,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","robotics","mlops","deep-learning","data-pipeline","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=8016984003#/8016984003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T17:27:51Z","expires_at":"2026-11-13T16:56:23.376677Z","created_at":"2026-10-14T15:20:30.457065Z","updated_at":"2026-10-14T16:56:23.379931Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/208f6a9b-9968-46f0-aa68-f972d885cce0"},{"id":"837f380f-cd9b-4105-97a6-3167f9b4d751","company_id":"a4de8095-cb98-4b8b-9bfc-c2d248f257ea","title":"Staff Data Scientist","slug":"staff-data-scientist-4fcb1aa8","description":"At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.  \n If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value. \n At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.\n If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.\n THE WORK\n We're looking for a Staff Data Scientist to be the technical lead across Ripple's diverse product and business portfolio. You'll define the analytics vision, build the scientific frameworks the org uses to evaluate product and business performance, and use AI tooling to accelerate the speed and reach of analytics across the company.\n In this role, you'll partner with product and business leads to frame the most important questions, set the analytical bar, and ensure decisions across surfaces rest on a consistent, thorough foundation. You'll operate as a force multiplier -- solving the hardest problems, building the frameworks others reuse, and leveling up data scientists across teams.\n WHAT YOU'LL DO\n \n Be the DS tech lead across multiple product and business teams: setting methodological standards and unblocking the hardest analytical problems across areas including Payments, Stablecoin \u0026 Custody products.\n Partner with product and business leads to define analytics strategy : shaping which initiatives to invest in, what success looks like, and how we'll know.\n Build the scientific frameworks Ripple reuses at scale : product and network health metrics, causal inference playbooks, liquidity and adoption models, and forecasting approaches that work across institutional and developer surfaces.\n Pioneer AI-accelerated analytics : applying LLMs and agentic workflows to scale insight generation, automate routine analysis, and enable self-serve exploration for non-DS partners.\n Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, surfacing the causal drivers behind adoption and volume.\n Define and communicate the metrics leadership runs on : translating complex results into clear narratives for Ripple executives and external stakeholders.\n Raise the bar for the DS function through thought leadership and mentorship across embedded teams.\n \n WHAT YOU'LL BRING\n \n 8+ years in data science or quantitative analysis, with a track record of senior level impact across multiple teams.\n Demonstrated technical leadership across cross-functional teams , influencing roadmaps and strategy at both executive and execution levels.\n Proven experience designing reusable analytics and measurement frameworks that scale across products.\n Hands-on experience applying AI to accelerate analytics workflows (agentic analysis, AI-assisted insight generation, natural-language data interfaces).\n Deep expertise in experimentation, causal inference, forecasting, and statistical modeling in a product environment.\n Expertise in Python or R, fluency in SQL, and experience with large-scale data tech (Databricks, Airflow, dBT a plus).\n Experience with FinTech, payments, crypto, or blockchain data is a strong plus.\n Advanced degree (MS, PhD) in a quantitative field preferred.\n Exceptional communication skills — able to turn technical depth into executive-ready narratives.\n For positions that will be based in CA, the annual salary range for this position is below. Actual salaries may vary based on numerous factors including, among other things, an individual applicant’s experience and qualifications for the position. This range does not include equity or additional compensation, such as bonuses or commissions. \n CA Annual Base Salary Range\n $212,000 — $265,000 USD \n \n WHO WE ARE: \n Do Your Best Work \n \n The opportunity to build in a fast-paced start-up environment with experienced industry leaders \n A learning environment where you can dive deep into the latest technologies and make an impact.  A professional development budget to support other modes of learning. \n Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify wit","salary_min":212000,"salary_max":265000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","fine-tuning","payments","llm","agents","data-science"],"apply_url":"https://ripple.com/careers/all-jobs/job/8260181?gh_jid=8260181","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:55:15Z","expires_at":"2026-11-13T16:59:24.161998Z","created_at":"2026-10-14T15:22:56.23564Z","updated_at":"2026-10-14T16:59:24.165197Z","company_name":"Ripple","company_slug":"ripple","company_logo_url":"https://www.google.com/s2/favicons?domain=ripple.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/837f380f-cd9b-4105-97a6-3167f9b4d751"},{"id":"25cc098c-d5a9-4722-8570-cdb9d8c94f57","company_id":"63bced38-3605-4e57-99f3-e213b2d40bf3","title":"Data Scientist II","slug":"data-scientist-ii-cdd4036b","description":"Data Scientist II, Cohere Health, Inc., Boston, Massachusetts (Remote) \n Duties: Gather business requirements and apply trend analysis to identify actionable insights using analytic tools. Analyze healthcare claims and authorization data to identify outliers, interpret patterns, and assess trends and opportunities. Apply large language models (LLMs), stochastic optimization methods, and related technologies to support decision-making. Monitor industry trends, regulatory changes, and emerging fraud schemes to support detection strategy development, maintain methodology documentation, and present findings to leadership. Train, fine-tune, and implement LLMs and deep learning models for tasks such as anomaly detection, classification, and automated analysis of security-related or operational data. Analyze datasets to assess expected impacts and return on investment, including effects on medical expense, administrative cost, and clinical outcomes. Develop data models, algorithms, and simulations to support analytical objectives. Build visualizations and dashboards to communicate analytical findings and support business workflows.\n This is a fully remote position and may be performed from anywhere within the United States. Occasional travel to company headquarters in the U.S. for onboarding, team meetings, and company events may be required.\n Requirements:\n Must have a Master’s degree in Data Science, Statistics, Biostatistics or related field (willing to accept foreign education equivalent) and 36 months of experience as Analyst or related position analyzing datasets.\n Must possess three (3) years of experience (may be gained concurrently with above experience) in the following skills:\n \n Analyzing healthcare datasets, including EMR/EHR data, medical and pharmacy claims data, and Social Determinants of Health (SDoH) data;\n Performing data quality analysis to identify data quality issues, gaps, or inconsistencies;\n Building scalable AWS Airflow pipelines to ingest, integrate, and transform terabytes of data using Python and PySpark and AWS S3 storage, and accelerate business decision-making in AWS Sagemaker; \n Utilizing trend identification and data analysis methods such as regression analysis and outlier detection model in python, spark and sql.;\n Building and implementing models, creating algorithms, and running simulations;\n Developing Tableau visualization and partnering with product owners and stakeholders to drive enterprise-level solutions that integrate data science into business workflows; \n Developing NLP, LLMs (extractive and generative), fine-tuning, and LLM model development.\n \n Salary range is $137,000.00 to $161,000.00 per year for a 40-hour work week.\n #LI-DNI\n -\n #BI-DNI\n -","salary_min":137000,"salary_max":161000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["healthcare","nlp","fine-tuning","cloud","llm","deep-learning","data-science"],"apply_url":"https://job-boards.greenhouse.io/coherehealth/jobs/8005450003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-05T19:10:55Z","expires_at":"2026-11-13T16:56:47.541154Z","created_at":"2026-10-14T15:20:32.691553Z","updated_at":"2026-10-14T16:56:47.544068Z","company_name":"Cohere Health","company_slug":"cohere-health","company_logo_url":"https://www.google.com/s2/favicons?domain=coherehealth.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/25cc098c-d5a9-4722-8570-cdb9d8c94f57"},{"id":"2d20090b-e536-4e32-8e91-f25675ac4950","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"AI Evaluation Infrastructure Engineer","slug":"ai-evaluation-infrastructure-engineer-4ec228d5","description":"It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.\n The Role \n We build AI products, and the quality of our evaluations sets the ceiling for how good those products can be. The speed of our evaluations determines how quickly we can improve them.\n We are looking for an engineer to build the infrastructure and tooling that make high-quality AI evaluation possible at Block's scale. You will help teams understand whether a model or product change is actually better, whether a result is statistically meaningful, and whether offline evaluation is predicting what happens with real users.\n Our evaluation approach combines offline evals that encode our definition of a good response, online evals that show how people actually respond, and a feedback loop that keeps the two converging. Your work will turn that approach into systems that product teams can use quickly, reliably, and with confidence.\n This is a high-impact, early-stage area with broad surface area. You will help decide what to build first, then build the platform that helps teams ship better AI products faster.\n You Will \n \n Build an execution engine that can score candidate versions against task sets in minutes, not hours.\n Create task set tooling that samples from production logs and validates tasks before they are admitted into an evaluation set.\n Build grader infrastructure across ground truth checks, rubrics, and LLM-as-judge approaches.\n Develop tooling that helps human reviewers calibrate judges, measure judge-to-human agreement, and monitor drift over time.\n Build leaderboards and reporting systems that include sample size, confidence intervals, and run-to-run variance, so teams can distinguish real improvements from noise.\n Support in-product side-by-side serving, feedback capture, and implicit signal extraction from real conversations.\n Build the loop that compares offline scores with online outcomes, identifies eval sets that have stopped predicting reality, and helps teams improve them.\n Partner with product, engineering, data, and ML teams to make evaluation workflows fast enough and trustworthy enough to become part of everyday development.\n \n You Have \n \n Experience building production platforms or infrastructure, including distributed batch execution, data pipelines, or systems that process production logs.\n Strong statistical literacy, including comfort with confidence intervals, variance, power, and multiple comparisons.\n The judgment to identify when a result is meaningful and when it is noise.\n Experience evaluating LLM or ML systems, or deep systems engineering experience with a strong interest in AI evaluation.\n Product instinct for internal tools. You understand that leaderboards, annotation tools, and workflows only matter if teams actually use them.\n A bias toward building reliable, observable systems that other engineers can trust.\n Strong collaboration skills and the ability to work across ambiguous product, data, and engineering problems.\n \n Success in your first year \n \n Product teams can stand up credible evals for new AI surfaces in days.\n Eval results gate CI and run quickly enough that engineers do not route around them.\n Judge-to-human agreement is measured, published, and monitored for drift.\n Launch decisions are not made on results that are within statistical noise.\n At least one case is documented where online reality disagreed with offline scores, and the evaluation was improved as a result.\n \n Why this matters \n AI product development moves quickly, but speed only helps when teams can trust the signal they are using to make decisions. This role will build the systems that make those signals faster, more accurate, and more actionable. Your work will directly influence how Block evaluates, improves, and ships AI products.\n  \n Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditio","salary_min":263600,"salary_max":395400,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","data-pipeline","payments","data-science","infrastructure","evaluation"],"apply_url":"http://block.xyz/careers/jobs/5434157008?gh_jid=5434157008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-30T18:33:32Z","expires_at":"2026-11-13T16:58:11.19989Z","created_at":"2026-10-14T15:21:51.219903Z","updated_at":"2026-10-14T16:58:11.202872Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d20090b-e536-4e32-8e91-f25675ac4950"},{"id":"912e6777-d679-4239-aea4-1e3caddfc47e","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Principal Machine Learning Engineer","slug":"principal-machine-learning-engineer-3e73a6c2","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n You'll set the technical direction for the intelligence layer every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. As a senior individual contributor with no direct reports, you will own strategy across the data graph, entity resolution, and buying intent, staying hands-on in the hardest of these problems. You'll decide when classical machine learning, statistics, or language models is the right tool, and turn good judgment into standards other teams adopt.\n What You'll Do \n \n You will set technical strategy for the data graph, entity resolution, buying intent, and agent context, deciding which problems to take on, in what order, and how the pieces connect.\n You will frame and ship problems nobody has framed yet, such as extraction for companies with no public footprint or semantic intent at web scale, deciding what not to build as deliberately as what to build.\n You will define evaluation standards that other teams adopt, including sampling methodology, human labels, validated LLM judges, and release gates, and know where each one breaks.\n You will define the data contracts between the data graph, intent signals, and the agents that consume them, including agent memory, keeping the system cost-effective to run across the full dataset.\n You will turn good choices into defaults across the group: the baseline every model must beat, how uncertainty is reported, and which metrics get retired when they mislead.\n You will lead design reviews, mentor senior engineers, shape hiring, and represent the team's technical judgment to product and leadership.\n \n What You Bring \n Must-Have: \n \n You have set technical direction as an individual contributor for a problem area spanning more than one team, with the resulting machine learning systems in production and owned after launch — depth of impact matters more than years or past titles.\n You bring breadth across classical machine learning on large, messy tabular data, applied statistics, language processing at scale, and LLM agents or multi-step systems, with recognized depth in at least one.\n You have defined and driven adoption of evaluation standards beyond your own team for systems with no single right answer, including LLM judges validated against human labels and results reported with their limits.\n You have a track record of changing how a group builds or measures — for example, introducing a baseline or review practice that other teams now use.\n You have made cost and capacity decisions for model training and serving, including when to self-host, distill, or call a hosted model, with cost tracked per unit of work.\n You are hands-on in production Python and strong SQL with distributed data processing experience, and you use AI coding tools daily with rigorous review of their output.\n \n Preferred: \n \n You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.\n You bring entity resolution, knowledge graph, or web-scale information extraction experience on messy, real-world data.\n You have trained and served open-weight models in PyTorch or an equivalent framework, including GPU capacity planning.\n You have built user memory or context systems for agents, or defended LLM systems against adversarial inputs and prompt injection.\n \n #LI-Remote \n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $192,500 — $302,500 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\n ZoomInfo may use a software-based assessment as part o","salary_min":192500,"salary_max":302500,"location":"Waltham, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","llm","agents","data-science","machine-learning"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8833507002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-28T16:55:31Z","expires_at":"2026-11-13T17:03:21.832514Z","created_at":"2026-10-14T15:25:25.398227Z","updated_at":"2026-10-14T17:03:21.835739Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/912e6777-d679-4239-aea4-1e3caddfc47e"},{"id":"c0052234-bd6e-4ec1-847e-cf309c220c0b","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Director of Data Science","slug":"director-of-data-science-a1930889","description":"Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.\n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, verifying and validating the autonomy stack, and defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis. \n Mission Summary: \n As the Director of Data Science, you will lead the strategic vision and execution for extracting intelligence from petabyte-scale on-road fleet data and simulation ecosystems. This leadership role is centered on building advanced data-driven methodologies, developing predictive and generative models, and serving as the primary analytical anchor supporting not only the Systems Readiness and Performance/Metrics teams but the whole company. You will drive the statistical rigor necessary to translate raw road data into high-fidelity performance metrics that directly inform vehicle launch readiness and safety-critical decisions. \n What You'll Be Doing: \n \n Rapid Operational Analytics: Execute fast-turnaround investigations on emergent fleet issues, converting real-time operational data into high-impact insights for launch decisions.\n Advanced Exposure Modeling: Build complex statistical models to evaluate vehicle safety and performance across diverse environmental variables and Operational Design Domains.\n Cross-Functional Partnership: Serve as the primary analytical anchor for Systems and Autonomy teams (Perception, Prediction, Planning).\n Executive Artifacts: Work with other systems teams to distill raw telemetry and rapid-response findings into high-level strategy documents, visualizations, and dashboards.\n Team Leadership: Mentor and scale a dedicated four-person team, instilling a culture that pairs rapid analytical agility with absolute scientific rigor. \n \n What We're Looking For: \n \n Industry Experience: 10+ years of professional experience leading data science or applied AI/ML initiatives, with at least 5 years explicitly scaling data-driven programs for autonomous driving, robotics, or complex safety-critical engineering systems.\n Educational Background: Master’s degree or PhD in Data Science, Computer Science, Robotics, Mathematics, Physics, or a highly quantitative field.\n Expertise in Advanced Analytics: Deep foundational knowledge in statistical analysis, density estimation, hypothesis testing, uncertainty quantification, and causal reasoning.\n Technical Proficiency: Mastery of Python, SQL, and data science infrastructure libraries, alongside a strong understanding of modern sequence modeling (Transformers) and distributed compute scaling frameworks (e.g., Ray, AWS).\n System Deconstruction: Demonstrated track record of translating ill-defined, ambiguous real-world performance questions into structured metric designs and concrete solution architectures.\n Exceptional Communication: Proven capacity to articulate highly technical data patterns and trade-offs into compelling strategies for cross-functional engineering peers and C-suite executives.\n Collaborative Leadership: Strong interpersonal ability to wield cross-functional technical influence, resolve technical disagreements across teams, and establish organizational best practices around data integrity. \n \n Bonus Points (not required): \n \n \n Experience designing reward functions or applying Inverse Reinforcement Learning (IRL) to evaluate human-like driving intent, assertiveness, and comfort.\n \n Familiarity with physical AI ecosystems, open-weights Vision-Language models (VLMs), or processing raw telemetry without text-translation bottlenecks.\n \n Direct experience evaluating core onboard software subsystems like Perception, Prediction, or Motion Planning.\n \n Deep familiarity with functional safety engineering standards like ISO 26262 and ISO 21448 (SOTIF)\n \n This is a unique opportunity to make a meaningful impact at Motional by enhancing our data understanding and accelerating development speed for our industry-leading autonomous vehicle metrics and capabilities ecosystem. We take pride in having some of the brightest and most dedicated talent in the world on our team. If you're innovative, driven, and eager to tackle challenges, we'd love to have you join us! \n Physical Demands: \n While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or ","salary_min":288000,"salary_max":396000,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","robotics","autonomous-vehicles","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=8005531003#/8005531003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T17:33:47Z","expires_at":"2026-11-13T16:56:23.308917Z","created_at":"2026-10-14T15:20:30.396025Z","updated_at":"2026-10-14T16:56:23.312167Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c0052234-bd6e-4ec1-847e-cf309c220c0b"},{"id":"6ec4683f-9f2f-41b8-b96a-d40885e616b9","company_id":"92df3417-f362-4f1a-9406-e34d8013b283","title":"Data Scientist, Payments","slug":"data-scientist-payments-98e3b94b","description":"Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together. So we expanded into software and started building integrated, omnichannel solutions – to help sellers sell online, manage inventory, offer buy now, pay later functionality, book appointments, engage loyal buyers, and hire and pay staff. Across it all, we’ve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale. Today, we are a partner to sellers of all sizes – large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.\n The Role \n We are looking for a quantitative, AI-fluent early-career professional to fill a data science role focused on payments data across Square and Cash App. You will partner closely with Product, Operations, Finance, and Engineering, leveraging data tools to optimize our payments platform from success rate, to cost efficiency, to risk mitigation. In addition to vertical work focused on the Payments domain, we also expect you to contribute to horizontal initiatives across the broader Data Org from data systems, to process and tooling.\n You Will \n \n Leverage AI to create scalable data tools, agents, and automations\n Partner with Product, Engineering, Finance, and Operations teams to systemize and make data-driven decisions\n Measure and optimize the cost and performance of our payments stack\n Serve as a subject-matter expert on our payments business and implementation\n Proactively identify gaps and opportunities in our processes\n Design, run, and analyze experiments\n Independently research questions and produce actionable insights\n Build ETL pipelines, dashboards, and machine learning models\n Translate open-ended business needs into practical data solutions\n Design, implement, and maintain data systems and system architecture\n Stand up, maintain, and improve cloud data infrastructure and tooling\n Operationalize data processes and systems\n \n You Have \n \n A bachelor’s degree in a quantitative field with a strong academic record\n Experience working with code at an internship\n Experience working with large datasets using code, including SQL and Python\n Experience using AI tools to accelerate development, improve data workflows, and automate processes\n Strong analytical expertise and business acumen\n Strong ability to communicate with non-technical partners\n Knowledge of systems architecture and cloud infrastructure\n Bonus: experience with payments or banking\n  \n Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.\n To find a location’s zone designation, please refer to this  resource . If a location of interest is not listed, please speak with a recruiter for additional information. \n  \n Zone A:\n $111,700 — $167,500 USD \n Zone B: \n $106,100 — $159,100 USD \n Zone C:\n $100,500 — $150,700 USD \n Zone D:\n $95,000 — $142,400 USD \n Application Guidelines \n Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.\n Use of AI in Our Hiring Process \n We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. \n Contact us here  with hiring practice or data usage questions.\n Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering.  Check out our other benefits at Block. \n Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people.  Square  makes commerce and financial services accessible to sellers.","salary_min":95000,"salary_max":142400,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["payments","data-pipeline","cloud","data-science"],"apply_url":"http://block.xyz/careers/jobs/5214784008?gh_jid=5214784008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-24T19:14:19Z","expires_at":"2026-11-13T16:58:11.213368Z","created_at":"2026-10-14T15:21:51.233663Z","updated_at":"2026-10-14T16:58:11.216092Z","company_name":"Block","company_slug":"block","company_logo_url":"https://www.google.com/s2/favicons?domain=block.xyz\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6ec4683f-9f2f-41b8-b96a-d40885e616b9"},{"id":"fb82e509-19e5-4dc2-ae8c-71b528954d2c","company_id":"7f070aa1-7d20-4bbe-b6d2-68769923074e","title":"Senior Director, Analytics \u0026 Data Science","slug":"senior-director-analytics-data-science-bbf7d8b0","description":"Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.\n\n\n\nWe're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.\n\n\n\nWe're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.\n\n\n\nIf you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.\n\n\n\nAbout the Role:\n\nAs Senior Director of Analytics \u0026 Data Science, you will play a key role in how Crusoe makes decisions. This is a new role leading a newly formed team of centralized analysts and data scientists who are embedded directly with the business units and domains they support. Your mandate spans cloud, customer experience, construction, power, manufacturing, and more. The analytics team owns the business logic, certified data models, and reporting that leadership relies on, and partners closely with our Data team, which owns and maintains the data platform and the ingestion of data from source systems.\n\n\n\nWe are seeking a hands-on leader who is energized by building something from the ground up. The ideal candidate is deeply technical, credible with executives and operators alike, and drawn to complex, cross-functional problems. Reporting into the CFO/COO organization, you will define the operating model, hire and develop the team, set the standards to maintain consistent metrics across the company, and be a key partner to leadership in making faster, better-informed decisions. In the near term, much of the work is foundational — building the data models and definitions that rigorous analysis depends on. As that foundation matures, the team will take on more forecasting, experimentation, and predictive work.\n\n\n\nWhat You'll Be Working On:\n\n - Stand Up the Central Team: Work with leaders across the business to understand their analytical needs, and design the staffing and operating model that meets them. Define how analysts are deployed, how work is prioritized, and how the team engages with each domain.\n\n - Build and Develop the Team: Recruit and grow analysts and data scientists who pair technical depth with business credibility. Build the career path and development model to retain talent.\n\n - Set Standards for Analytical Rigor: Define how the team approaches statistical and modeling work, including experimental design, forecasting, and how uncertainty is communicated to decision-makers. Review your team's methods substantively, and work hands-on where needed.\n\n - Build the Data Science Capability: Determine where predictive and statistical methods will have the most impact and sequence the investment accordingly. Near-term opportunities span power utilization, capacity and demand forecasting, schedule and cost forecasting, fleet reliability, and product experimentation.\n\n - Own Certified Marts and Metric Definitions: Own the business logic, certified data marts, metric dictionary, and business-rule testing that keep metrics consistent across the company. Establish peer review as a standard practice so work is checked by qualified colleagues before it reaches business leaders.\n\n - Own Executive and Cross-Business Reporting: Own certified reporting for the executive team, the board, and questions that span business units, with operational reporting in the business units built on the marts your team certifies.\n\n - Drive High-Stakes Decision Support: Turn the company's most complex, cross-functional questions into clear analysis and recommendations that leadership can act on.\n\n - Build Toward Self-Service: Create the conditions for the business to answer routine questions without routing through an analyst, including the table and field descriptions our data MCP reasons over, on top of certified marts with properly defined metrics.\n\n - Maintain Close Proximity to the Business: Keep analysts embedded in their domains through staff meetings, planning cycles, and shared goals, and align central and business unit priorities so analysts always know what matters most.\n\n - Partner with the Data Team: Collaborate with the Data team on the data foundation, contributing the domain context and business definitions that help new source s","salary_min":310000,"salary_max":355000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["code-generation","data-pipeline","llm","data-science"],"apply_url":"https://jobs.ashbyhq.com/crusoe/a47eea20-0f86-4d60-ab86-4e4f9ad9abc7/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-22T03:59:50.784Z","expires_at":"2026-11-13T16:56:14.621605Z","created_at":"2026-10-14T15:20:22.863893Z","updated_at":"2026-10-14T16:56:14.624478Z","company_name":"Crusoe","company_slug":"crusoe","company_logo_url":"https://www.google.com/s2/favicons?domain=crusoe.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fb82e509-19e5-4dc2-ae8c-71b528954d2c"},{"id":"7ed29a1e-257e-49b4-bb15-d6660ee8acb0","company_id":"63bced38-3605-4e57-99f3-e213b2d40bf3","title":"Senior Manager, Applied Science","slug":"senior-manager-applied-science-610e03c6","description":"Opportunity Overview:  \n As the Sr. Manager, Applied Science, you will play a key role in building and scaling Cohere Health's AI capabilities. You'll lead a team of Applied Scientists focused on developing, evaluating, and deploying models that automate and augment complex clinical and administrative workflows.\n This team is responsible for the full lifecycle of applied machine learning from problem framing, rigorous attention to methods and experimentation, to partnering with AI Engineering to deploy and monitor models. You'll work with structured and unstructured healthcare data to generate insights and power intelligent systems that improve prior authorization and broader clinical decision-making.\n As a player-coach, you'll combine hands-on technical contributions with team leadership, helping to establish best practices, mentor team members, and drive high-impact solutions in close partnership with Product, Engineering, Clinical, and Analytics stakeholders. This is an opportunity to shape both the technical direction and team culture within a growing global organization.\n This is a remote-first role that may require travel to Boston, MA for new hire onboarding and occasional in-person team meetings and company events.\n What you'll do: \n \n Lead, mentor, and develop a team of Applied Scientists, fostering a collaborative, high-performance environment\n Translate complex healthcare business and clinical problems into scalable machine learning and data science solutions\n Validate those solutions with methodological and statistical rigor to assure that our Clinical AI is reliable, reproducible, and safe\n Partner with AI Engineering to scale those solutions to create real healthcare impact!\n Partner cross-functionally with Clinical Operations, Product, Engineering, and platform teams to align technical research and validation work with business priorities\n Communicate technical concepts, methodologies, and insights clearly to both technical and non-technical stakeholders\n Contribute to recruiting, onboarding, and developing team members\n \n What you'll need: \n Must-haves\n \n PhD with 7+ years experience or MS with 10+ years experience in machine learning, data science, or applied AI roles, of those a minimum 2+ years of experience leading and managing technical teams\n Experiencing leveraging both modern language models (agentic or non-agentic LLM) and classical ML techniques in a production setting\n Expertise in statistical methods, experimental design, and model evaluation\n Proficiency in Python and experience with ML frameworks (e.g., PyTorch, scikit-learn)\n AWS experience\n Demonstrated track record of driving decision making by collaborating with business functions and product to accurately determine problem statements, form falsifiable hypotheses, and apply robust experimentation and analyses to drive toward valuable and robust solutions\n \n Nice-to-haves\n \n Experience in transactional healthcare (e.g., Utilization Management, Payment Integrity, Risk Adjustment, Quality, Care Management, etc)\n Experience driving process automation in a healthcare setting using modern language models\n Experience applying retrieval and relational extraction techniques to unstructured clinical notes and other patient history documentation\n \n Pay \u0026 Perks: \n 💻 Fully remote opportunity with about 15% travel\n 🩺 Medical, dental, vision, life, disability insurance, and Employee Assistance Program \n 📈 401K retirement plan with company match; flexible spending and health savings account \n 🏝️ Flex Time Off + company holidays\n 👶 Up to 14 weeks of paid parental leave \n 🐶 Pet insurance  \n The salary range for this position is $225,000 to $240,000 annually; as part of a total benefits package which includes health insurance, 401k and bonus. In accordance with state applicable laws, Cohere is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including but not limited to qualifications for the role, experience level, skillset, and internal alignment. \n Interview Process*: \n \n Connect with Talent Acquisition for a Preliminary Phone Screening\n Meet your Hiring Manager!\n Behavioral Interview(s)\n Case Study\n \n *Subject to change\n About Cohere Health: \n Cohere Health’s clinical intelligence platform and agentic AI-powered solutions connect health plans’ strategic goals and providers’ needs, optimizing the speed, cost, and quality of care. With an enterprise approach that streamlines payer-provider decision-making across the care continuum–including policy, prior authorization, payment accuracy, and more–the company improves collaboration and reduces burden, resulting in up to 8x ROI and 94% provider satisfaction. \n With the acquisition of ZignaAI, we’ve further enhanced our platform by launching our Payment Integrity Suite, anchored by Cohere Validate™, an AI-driven clinical and coding validation solutio","salary_min":225000,"salary_max":240000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","payments","healthcare","pytorch","agents","cloud","data-science","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/coherehealth/jobs/8001171003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-21T23:10:50Z","expires_at":"2026-11-13T16:56:47.664764Z","created_at":"2026-10-14T15:20:32.795062Z","updated_at":"2026-10-14T16:56:47.667891Z","company_name":"Cohere Health","company_slug":"cohere-health","company_logo_url":"https://www.google.com/s2/favicons?domain=coherehealth.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7ed29a1e-257e-49b4-bb15-d6660ee8acb0"},{"id":"1d949a93-d678-4755-b0f7-aca698eb1c9a","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-d1dd4c03","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n \n You'll help build the intelligence every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. You'll own outcomes end to end, from the data a model learns from to the product surface where its output lands, applying classical machine learning, statistics, or language models based on what the problem calls for. You'll take a problem in the data graph, entity resolution, or buying intent from design through production, and own it after launch.\n What You'll Do \n \n You will extract leadership, locations, and products from the websites of companies with almost no public footprint, and detect stale records, choosing which pages to read based on cost per document.\n You will estimate headcount and revenue for companies with sparse public information, using gradient-boosted trees, regression with missing inputs, and calibrated uncertainty.\n You will determine whether two records describe the same company or person, measuring both false merges and false splits.\n You will infer buying intent from the meaning of what companies read, not keyword matches, across large volumes of multilingual, noisy text, feeding propensity scoring, lookalike retrieval, and contact recommendations.\n You will build research agents that cite their sources, along with the evaluation systems behind them: human labels, validated LLM judges, and regression gates.\n You will train small, task-specific models distilled from larger ones, measuring them against the larger model rather than against perfection, and own their quantization and serving.\n You will take a problem from design to production and own it after launch, including its evaluation and its cost, while helping other engineers improve their work through design and code review.\n \n What You Bring \n Must-Have: \n \n You have taken machine learning systems to production and owned them after launch, following the outcome into whichever layer it needs — depth matters more than years.\n You bring classical machine learning expertise beyond language models, including supervised learning and feature engineering on large, messy tabular data, along with applied statistics: experiment design, statistical inference, and calibrated scores under class imbalance.\n You have deployed language processing at scale — text classification, information extraction, and entity linking over large volumes of multilingual, noisy text.\n You have built LLM agents or multi-step systems in production, including tool and context design and failure analysis from traces, and you've evaluated systems with no single right answer using LLM judges validated against human labels.\n You are proficient in production Python and strong in SQL, with experience in distributed data processing, and you use AI coding tools daily, rigorously reviewing their output.\n \n Preferred: \n \n You have experience with ranking and retrieval, including embeddings, learned re-ranking, and metrics such as recall@k, MRR, and nDCG.\n You bring experience in propensity modeling, clustering, or entity resolution on messy, real-world data.\n You have trained and served open-weight models in PyTorch or an equivalent framework, with cost tracked per unit of work.\n You have defended LLM systems against adversarial inputs and prompt injection, or worked on web-scale information extraction, knowledge graphs, or user memory for agents.\n \n  \n  \n #LI-Remote\n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $128,100 — $201,300 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\n ZoomInfo may use ","salary_min":128100,"salary_max":201300,"location":"Bethesda, MD","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["nlp","pytorch","agents","llm","machine-learning","data-science"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8769474002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-21T18:38:41Z","expires_at":"2026-11-13T17:03:21.861603Z","created_at":"2026-10-14T15:25:25.428017Z","updated_at":"2026-10-14T17:03:21.864592Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d949a93-d678-4755-b0f7-aca698eb1c9a"},{"id":"23daf693-4e85-4e7b-8c0c-ffdfd968c195","company_id":"43dd17db-bec3-4ebb-a432-f71d57a9aa47","title":"Senior Manager / Director, Statistical Genetics","slug":"senior-manager-director-statistical-genetics-f0d488c3","description":"THE OPPORTUNITY\n\ninsitro's mission is to bring better drugs faster to the patients who can benefit most, through machine learning and data at scale. Our strategy combines causal evidence from human genetics with in vitro cellular data from our automated wet-lab platforms to find causal genetic intervention points and carry them into therapeutic programs.\n\nIn this role you'll lead and grow an established team of statistical geneticists responsible for advancing our machine learning statistical genetics platform and using it to extract novel biological insight. You'll partner closely with ML scientists to develop novel phenotypes, with software engineers to build robust tools, and with computational biologists and therapeutic area scientists to turn genetic signals into programs. As the leader of this team, you'll set both strategic and technical direction, and structure the collaborations that move insitro's programs forward through a deeper understanding of causal human biology.\n\nThis position reports to the Senior Director, AI/ML (Human Genetics), and is remote-eligible within the US or based in South San Francisco with an in-person hybrid schedule of three days per week.\n\n \n\n\nRESPONSIBILITIES\n\nTeam \u0026 Technical Leadership\n\n - Lead the Team: Lead, mentor, and grow a team of four statistical geneticists developing methods to extract insight from human genetics\n\n - Set the Direction: Define the roadmap and priorities for statistical genetics at insitro, and hold the bar for rigor\n\nMethods \u0026 Platform Innovation\n\n - Advance the Methods: Lead the team in building new ML methodology that extracts more signal from human genetics and integrates it with other data modalities\n\n - Make It Reusable: Partner with software engineering to turn one-off analyses into well-tested workflows the whole research organization can run\n\nTarget Discovery \u0026 Translation\n\n - Own the Analysis: Own GWAS, rare variant, and post-GWAS analyses across internal indications to identify and prioritize novel targets, moving from association to causal hypothesis\n\n - Translate to Programs: Partner with therapeutic area and drug discovery scientists to convert genetic findings into validated, tractable programs\n\n \n\n\nABOUT YOU\n\nExperience \u0026 Qualifications\n\n - Proven Tenure: PhD in statistical genetics, human genetics, computational biology, biostatistics, or a related field, plus 5+ years of experience\n\n - Statistical Genetics Depth: Deep knowledge of statistical genetics as applied to target discovery in an industry setting\n\n - People Leadership: Direct people management experience, with a track record of mentoring and growing technical talent\n\n - ML Fluency: Demonstrated experience applying machine learning to genetic, imaging, or other clinical data\n\n - Published Innovator: A history of publication and methodological innovation in statistical genetics\n\nCore Competencies\n\n - Cross-functional Communicator: You communicate clearly and collaborate easily across functions, including with experimental scientists and software engineers\n\nPreferred Qualifications\n\n - Engineering Fluency: Strong Python or R in a cloud environment, with a track record of reusable, tested, version-controlled code\n\n - Disease Area Depth: Real understanding of a disease area relevant to insitro — cardiovascular, metabolic, liver, ophthalmologic, or neurological\n\n - Discovery Track Record: Genetic discoveries that have shaped a real drug discovery decision or program\n\n - Multi-modal Experience: Work with high-content modalities such as single-cell or bulk omics, genetic perturbation screening, spatial proteomics, or pooled optical screening\n\n \n\n\nCOMPENSATION \u0026 BENEFITS AT INSITRO\n\nOur target starting salary for successful US-based applicants for this role is $224,000 - $273,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.\n\nThis role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.\n\n \n\nIn addition, insitro also provides our employees:\n\n - 401(k) plan with employer matching for contributions\n\n - Excellent medical, dental, and vision coverage as well as mental health and well-being support\n\n - Open, flexible vacation policy\n\n - Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)\n\n - Quarterly budget for books and online courses for self-development\n\n - New hire stipend for home office setup\n\n - Monthly cell phone \u0026 internet stipend\n\n - Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid\n\n - Access to a free commuter bus network that provides transport to and fr","salary_min":224000,"salary_max":273000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","payments","machine-learning","data-science"],"apply_url":"https://jobs.ashbyhq.com/insitro/1253d6c7-f9ce-4bda-ab08-2abe1a9fbfd0/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-18T17:51:49.805Z","expires_at":"2026-11-13T16:56:54.126214Z","created_at":"2026-10-14T15:20:38.864688Z","updated_at":"2026-10-14T16:56:54.129691Z","company_name":"Insitro","company_slug":"insitro","company_logo_url":"https://www.google.com/s2/favicons?domain=insitro.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/23daf693-4e85-4e7b-8c0c-ffdfd968c195"},{"id":"61beaeaa-00d3-4315-bd31-d3e797ccb617","company_id":"b4787255-dacd-444b-8e44-bb9971ec1f36","title":"Director, Applied AI","slug":"director-applied-ai-23ed1532","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.\n \n You'll lead the team that builds the intelligence every ZoomInfo AI agent reasons over — what's true about companies and the people in them, how they relate, and what they're buying. This role owns the B2B data graph strategy end to end, blending classical machine learning and data science with LLM and agentic systems, and treating evaluation and inference cost as core disciplines. You'll set the technical bar for a team of senior engineers while staying close to the code, shipping alongside the people you lead.\n What You'll Do \n \n You will ship code alongside your team, prototype alone to prove or kill an idea, and review code as a peer, setting how the team uses agentic coding tools with precise specifications and rigorous review.\n You will own delivery end-to-end, from problem framing through serving and on-call, including long-tail graph coverage and user memory for agents that separates user-supplied context from system-of-record data.\n You will choose the right method for each problem — classical machine learning, language models, or code — for challenges like sparse-company revenue estimation, entity resolution, and semantic intent modeling, deciding on measured evidence and stopping work that won't pay off.\n You will define what it takes to claim an agent's output is correct, not just that its run completed, building the evaluation datasets, regression gates, and experiment designs that back those claims.\n You will own inference cost, latency, and capacity, including build-versus-buy and distillation decisions, since a model too expensive to run everywhere isn't a result.\n You will hire and grow machine learning engineers, data scientists, and research engineers, developing senior engineers into technical leaders.\n You will work across product, platform, security, and legal, and present results and their limits to executives, including when a system isn't good enough to launch.\n \n What You Bring \n Must-Have: \n \n You have significant experience building production machine learning systems and leading the engineers who build them by shipping alongside them — demonstrated capability matters more than years.\n You have a track record of hiring and developing senior machine learning engineers and data scientists against a high bar.\n You stay hands-on today: shipping code, building prototypes independently, and using agentic coding tools daily with rigorous review.\n You bring deep classical machine learning and data science expertise (supervised learning, feature engineering, statistical inference and experiment design, strong SQL) alongside production LLM and agentic systems, with the judgment to choose between them, including setting an evaluation bar with leakage-safe validation, calibration, and validated LLM judges.\n You have a record of cost and capacity decisions for model serving, such as moving a workload from a hosted model to a distilled or self-hosted one, with the savings measured, and you report results to executives with their limits stated, including recommending against a launch.\n \n Preferred: \n \n You bring entrepreneurial experience — founding a company, or taking a product from inception to paying customers as a founding or early engineer.\n You have experience in propensity modeling, ranking and retrieval, clustering, or entity resolution at scale.\n You have worked on web-scale language processing over multilingual, noisy text, knowledge graphs, or user memory for agents.\n You have experience with post-training and distillation, open-weight model serving, or AI governance and safety practices (ISO/IEC 42001, NIST AI RMF).\n \n  \n #LI-Hybrid\n #LI-VC1\n Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\n In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here .\n Below is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n $233,100 — $366,300 USD \n About us:  \n ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\n ZoomInfo is","salary_min":233100,"salary_max":366300,"location":"Bethesda, MD","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","search","agents","llm","data-science"],"apply_url":"https://www.zoominfo.com/careers?gh_jid=8800796002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-17T19:35:26Z","expires_at":"2026-11-13T17:03:21.806999Z","created_at":"2026-10-14T15:25:25.376569Z","updated_at":"2026-10-14T17:03:21.810754Z","company_name":"ZoomInfo","company_slug":"zoominfo","company_logo_url":"https://www.google.com/s2/favicons?domain=zoominfo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/61beaeaa-00d3-4315-bd31-d3e797ccb617"},{"id":"af1e3d36-7e37-4fca-a21d-7b449fcddd05","company_id":"a4de8095-cb98-4b8b-9bfc-c2d248f257ea","title":"Senior Data Scientist","slug":"senior-data-scientist-89849d61","description":"At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.  \n If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value. \n At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.\n If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.\n The work:\n We're looking for a Senior Data Scientist to drive analytics across Ripple's product and business portfolio. You'll build the scientific frameworks teams use to evaluate product and business performance, and use AI tooling to accelerate the speed and reach of your analysis.\n In this role, you'll partner with product and business leads to frame the right questions, bring rigor to how they're answered, and make sure decisions rest on a consistent, thorough foundation. You'll take on ambiguous, high-impact problems, build analyses and tooling others can reuse, and raise the analytical bar of the teams you work with.\n What you’ll do:\n \n Serve as the data science lead for one or more product or business areas — Payments, Stablecoin, or Custody — applying strong methodology and tackling the hardest analytical problems in your domain.\n Partner with product and business leads to shape roadmap decisions: which initiatives to prioritize, what success looks like, and how we'll measure it.\n Build the scientific frameworks your teams rely on: product and network health metrics, causal inference approaches, and forecasting that holds up across institutional and developer surfaces.\n Apply AI to accelerate analytics — using LLMs and agentic workflows to scale insight generation, automate routine analysis, and make data more self-serve for non-DS partners.\n Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, surfacing the causal drivers behind adoption and volume.\n Define and communicate the metrics your teams and leadership run on, translating complex results into clear narratives for senior stakeholders.\n Raise the bar for the DS function through mentorship and by modeling strong analytical practice for the data scientists around you.\n \n What you'll bring:\n \n 7+ years in data science or quantitative analysis, with a track record of measurable impact on product and business decisions.\n Experience as the data science partner to cross-functional teams, shaping roadmaps and helping leaders make better-informed calls.\n Track record designing analytics and measurement frameworks that other teams adopt and build on.\n Hands-on experience applying AI to accelerate analytics workflows — agentic analysis, AI-assisted insight generation, natural-language data interfaces.\n Strong expertise in experimentation, causal inference, forecasting, and statistical modeling.\n Expertise in Python or R, fluency in SQL, and experience with large-scale data tech (Databricks).\n Experience with FinTech, payments, crypto, or blockchain data is a strong plus.\n Advanced degree (MS, PhD) in a quantitative field preferred.\n Excellent communication skills — able to translate technical depth into clear narratives for senior stakeholders.\n For positions that will be based in CA, the annual salary range for this position is below. Actual salaries may vary based on numerous factors including, among other things, an individual applicant’s experience and qualifications for the position. This range does not include equity or additional compensation, such as bonuses or commissions. \n CA Annual Base Salary Range\n $172,000 — $215,000 USD \n \n WHO WE ARE: \n Do Your Best Work \n \n The opportunity to build in a fast-paced start-up environment with experienced industry leaders \n A learning environment where you can dive deep into the latest technologies and make an impact.  A professional development budget to support other modes of learning. \n Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify with, every employee is a respected, valued, and empowered part of the team. \n In-office collaboration for moments that matter is impo","salary_min":172000,"salary_max":215000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","payments","healthcare","agents","data-science"],"apply_url":"https://ripple.com/careers/all-jobs/job/8201766?gh_jid=8201766","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-14T22:19:17Z","expires_at":"2026-11-13T16:59:24.141779Z","created_at":"2026-10-14T15:22:56.218527Z","updated_at":"2026-10-14T16:59:24.145028Z","company_name":"Ripple","company_slug":"ripple","company_logo_url":"https://www.google.com/s2/favicons?domain=ripple.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/af1e3d36-7e37-4fca-a21d-7b449fcddd05"},{"id":"2455732f-ffe5-41cb-a4d2-c26f378224ee","company_id":"dcf03132-cd3a-4108-8e1d-20ab36008ea2","title":"Sr Data Scientist","slug":"sr-data-scientist-0246be73","description":"Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack - empowering organizations to run AI across multi-vendor environments with centralized governance. The world’s leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog , LinkedIn , X , and YouTube .\n The role of a Data Scientist at Dataiku is unique. Our Data Scientists develop solutions to real-world problems and participate in client-facing endeavours throughout the customer journey. This includes supporting their discovery of the platform, helping integrate Dataiku with other tools and technologies, providing user training, and co-developing data science projects from design to deployment. \n Just as non-technical skills are important, so are technical skills. Our Data Scientists work on the Dataiku platform daily. Aside from the visual tools, our team mostly uses Python, with occasional work in other languages (e.g., R, SQL, PySpark, JavaScript). An ideal candidate is excited to teach customers data science, how to use the Dataiku platform, and learn about new technologies. \n How you'll make an impact \n \n Help users discover and master the Dataiku platform via user training, office hours, demos, and ongoing consultative support. \n Analyse and investigate various data and machine learning applications across industries and use cases. \n Provide strategic input to the customer and account teams to help our customers succeed. \n Scope and co-develop production-level data science projects with our customers. \n Mentor and help educate data scientists and other customer team members to aid in career development and growth. \n \n What you'll need to be successful \n \n Curiosity and a desire to learn new technical skills \n Empathy and an eagerness to share your knowledge with your colleagues, Dataiku’s customers, and the general public \n Ability to clearly explain complex topics to technical as well as non-technical audiences \n Over 5 years of experience with coding (Python, R, SQL) \n Over 5 years of experience building ML models \n Understanding underlying data systems and platform mechanics, such as Cloud architectures, K8S, Spark, and SQL \n \n What will make you stand out \n \n Experience with Consulting and/or Customer-facing Data Science roles \n Experience with Data Engineering or MLOps \n Experience developing WebApps in JavaScript, RShiny, or Dash \n Experience building APIs \n Experience using enterprise data science tools \n Passion for teaching or public speaking \n \n #LI-Remote \n Compensation and Benefits \n The final compensation package for this role will be determined during the interview process and is based on a variety of factors, including, but not limited to, geographic location, internal equity, education, skill set, experience and training. Eligible roles may also be entitled to receive commission or other variable compensation through Dataiku's incentive compensation program.  \n Dataiku also offers comprehensive benefits, including stock options, medical, dental, and vision plans, flexible spending accounts, pre-tax commuter benefits, a 401k company match, paid vacations and sick leave, paid parental leave, employer paid disability coverage, and additional health and wellbeing perks and benefits. Dataiku reserves the right to amend or modify employee perks and benefits at any time. \n US only national base pay ranges\n $176,000 — $220,000 USD \n  \n What are you waiting for! \n At Dataiku, you'll be part of a journey to shape the ever-evolving world of AI. We're not just building a product; we're crafting the future of AI. If you're ready to make a significant impact in a company that values innovation, collaboration, and your personal growth, we can't wait to welcome you to Dataiku! And if you’d like to learn even more about working here, you can visit our Dataiku LinkedIn page .\n  \n Our practices are rooted in the idea that everyone should be treated with dignity, decency and fairness. Dataiku also believes that a diverse identity is a source of strength and allows us to optimize across the many dimensions that are needed for our success. Therefore, we are proud to be an equal opportunity employer. All employment practices are based on business needs, without regard to race, ethnicity, gender identity or expression, sexual orientation, religion, age, neurodiversity, disability status, citizenship, veteran status or any other aspect which makes an individual unique or protected by laws and regulations in the locations where we operate. This applies to all policies and procedures related to recruitment","salary_min":176000,"salary_max":220000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","cloud","mlops","agents","data-science"],"apply_url":"https://job-boards.greenhouse.io/dataiku/jobs/6186412004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-14T05:15:34Z","expires_at":"2026-11-13T16:57:38.880436Z","created_at":"2026-09-14T13:38:13.539282Z","updated_at":"2026-10-14T16:57:38.883103Z","company_name":"Dataiku","company_slug":"dataiku","company_logo_url":"https://www.google.com/s2/favicons?domain=dataiku.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2455732f-ffe5-41cb-a4d2-c26f378224ee"},{"id":"e80080e6-2342-4827-9a4e-0775b43935f0","company_id":"1b11016d-90e6-4447-9bc0-0b39a66b8268","title":"Senior Data Scientist - Risk ML","slug":"senior-data-scientist-1f1794da","description":"In 1999, NASA lost contact with its Mars Climate Orbiter after a 9-month journey from Earth. It began its planned orbital insertion maneuver but went out of radio contact after passing behind Mars. While we may never know whether it was destroyed in the atmosphere or re-entered heliocentric space, we can draw the lesson that getting the details (in this case, units) right is critical, especially when shooting for the stars.\n While Mercury’s cosmic journey may be more metaphorical, we have our own sky-high ambitions and the need to marry those with precise data analysis.\n To that end, we are hiring a Machine Learning-focused Data Scientist to support our Risk team. This team is responsible not only for detecting, monitoring, and mitigating both first- and third-party fraud but also ensuring we know and understand our customers while monitoring their behavior for financial crime risk. You’ll play a key role in strengthening our fraud defenses while ensuring that Mercury continues to deliver a smooth and trustworthy banking* experience.\n This is an opportunity to join Mercury at a pivotal moment in our growth. You’ll be working on some of the most critical challenges facing the business and collaborating across product, engineering, and risk to protect our customers and the financial system at large.\n Here are some things you’ll do on the job:\n \n Build, validate, and deploy machine learning models to identify and prevent fraud in real time\n Support the reproducibility and robustness of said models through documentation, testing, and monitoring\n Ensure data quality and reliability across pipelines and tools\n Collaborate with Risk Strategy to ideate on model inputs and applications and with Engineering optimize deployment and observability\n \n You should have:\n \n 5+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 3+ years of ML experience\n Proficiency in SQL and experience using it to understand and manage imperfect data\n Proficiency in Python and experience with statistical modeling and machine learning\n Experience deploying and monitoring machine learning models in production\n Comfort working in a fast-paced environment with evolving priorities\n \n Ideally you also have:\n \n 1+ years of relevant risk experience\n Familiarity with LLMs or other GenAI and how they can be applied to risk or fraud detection\n Experience with modern data tools for pipelines and ETL (e.g., dbt)\n Experience with model governance as required in finance or other regulated industries\n Experience building zero-to-one solutions in ambiguous or greenfield problem spaces\n \n *Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.\n Mercury values diversity \u0026 belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.\n #LI-AC1\n Total Rewards The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.\n Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.\n Our target new hire base salary ranges for this role are the following : \n US employees (any location):\n $166,600 — $250,900 USD \n Canadian employees (any location):\n $157,400 — $237,100 CAD","salary_min":157400,"salary_max":237100,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["llm","generative-ai","payments","data-pipeline","data-science"],"apply_url":"https://job-boards.greenhouse.io/mercury/jobs/6179231004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-02T15:44:30Z","expires_at":"2026-11-13T16:58:02.250489Z","created_at":"2026-09-03T13:40:17.521378Z","updated_at":"2026-10-14T16:58:02.253776Z","company_name":"Mercury","company_slug":"mercury","company_logo_url":"https://www.google.com/s2/favicons?domain=mercury.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e80080e6-2342-4827-9a4e-0775b43935f0"},{"id":"90590856-24b1-4445-8b2d-63b7adb92689","company_id":"1b11016d-90e6-4447-9bc0-0b39a66b8268","title":"Staff Data Scientist - Risk ML","slug":"staff-data-scientist-66a45504","description":"In 1999 NASA lost contact with its Mars Climate Orbiter after a 9 month journey from Earth. It began its planned orbital insertion maneuver but went out of radio contact after passing behind Mars. While we may never know whether it was destroyed in the atmosphere or re-entered heliocentric space, we can draw the lesson that getting the details (in this case, units) right is critical, especially when shooting for the stars.\n While Mercury’s cosmic journey may be more metaphorical, we have our own sky-high ambitions and the need to marry those with precise data analysis.\n To that end, we are hiring a Machine Learning-focused Data Scientist to support our Risk team. This team is responsible not only for detecting, monitoring, and mitigating both first- and third-party fraud but also ensuring we know and understand our customers while monitoring their behavior for financial crime risk. You’ll play a key role in strengthening our fraud defenses while ensuring that Mercury continues to deliver a smooth and trustworthy banking* experience.\n This is an opportunity to join Mercury at a pivotal moment in our growth. You’ll be working on some of the most critical challenges facing the business and collaborating across product, engineering, and risk to protect our customers and the financial system at large.\n Here are some things you’ll do on the job:\n \n Build, validate, and deploy machine learning models to identify and prevent fraud in real time\n Support the reproducibility and robustness of said models through documentation, testing, and monitoring\n Ensure data quality and reliability across pipelines and tools\n Collaborate with Risk Strategy to ideate on model inputs and applications and with Engineering optimize deployment and observability\n Act as a technical lead prototyping, iterating on, and codifying best practices - and bringing the rest of the team along\n \n You should have:\n \n 7+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 5+ years of ML experience\n Proficiency in SQL and experience using it to understand and manage imperfect data\n Proficiency in Python and experience with statistical modeling and machine learning\n Experience deploying and monitoring machine learning models in production\n Comfort working in a fast-paced environment with evolving priorities\n Demonstrated ability to lead and empower others, delivering not just on your own work, but upleveling those around you\n The ability to drive strategic alignment between teams with differing roadmaps, timelines, or architectures\n \n Ideally you also have:\n \n 1+ years of relevant risk experience\n Familiarity with LLMs or other GenAI and how they can be applied to risk or fraud detection\n Experience with modern data tools for pipelines and ETL (e.g., dbt)\n Experience with model governance as required in finance or other regulated industries\n Experience building zero-to-one solutions in ambiguous or greenfield problem spaces\n \n *Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.\n Mercury values diversity \u0026 belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.\n #LI-AC1\n Total Rewards The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.\n Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.\n Our target new hire base salary ranges for this role are the following : \n US employees (any location):\n $239,000 — $298,800 USD \n Canadian employees (any location):\n $225,900 — $282,400 CAD","salary_min":225900,"salary_max":282400,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","generative-ai","llm","payments","data-science"],"apply_url":"https://job-boards.greenhouse.io/mercury/jobs/6178814004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-02T14:06:51Z","expires_at":"2026-11-13T16:58:02.268169Z","created_at":"2026-09-03T13:40:17.617278Z","updated_at":"2026-10-14T16:58:02.271114Z","company_name":"Mercury","company_slug":"mercury","company_logo_url":"https://www.google.com/s2/favicons?domain=mercury.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/90590856-24b1-4445-8b2d-63b7adb92689"},{"id":"61180fc0-0978-4bdb-ba3e-773344379c4b","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Data Scientist, Simulation Capacity Optimization","slug":"senior-data-scientist-simulation-capacity-optimization-ad40467d","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Our Simulation team is at the heart of this mission, enabling us to safely and rapidly iterate on the Waymo Driver. We run billions of miles of simulations, creating a massive and complex demand for technical infrastructure resources (CPU, GPU, TPU, Storage).\n We are establishing a new team called CORPIO (SimEval Capacity Operations, Resource Planning, Infrastructure Optimization). This team is tasked with building a critical capability for Waymo: data-driven, strategic capacity planning and resource optimization. We are looking for a Senior Data Scientist to bridge the gap between sophisticated mathematical modeling and production-scale infrastructure automation. You will be responsible for building the technical systems that forecast demand, optimize resource allocation, and automate infrastructure management, ensuring our simulation environment is both high-performance and cost-effective.\n As a Senior Data Scientist on the CORPIO team, you will: \n \n Infrastructure Modeling \u0026 Automation: Design and build production-grade systems and pipelines to automate capacity planning, demand management, and quota allocation.\n Quantitative Forecasting: Implement and maintain sophisticated models for infrastructure demand forecasting, incorporating architectural shifts, peak loads, and time-shifting opportunities.\n Resource Optimization Algorithms: Develop and deploy algorithms to optimize resource utilization across a heterogeneous fleet (CPU, GPU, TPU) and diverse supply models (on-demand vs. reserved).\n Data Pipeline Engineering: Architect and maintain robust data pipelines that ingest infrastructure telemetry and demand driver signals to feed forecasting and optimization engines.\n Outcome Analysis: Build systems to translate resource plans into tangible outcomes (e.g., queue lengths, user demand fulfillment) and develop attribution models for capacity imbalances.\n Cross-Functional Collaboration: Partner with Simulation, Infrastructure, and Finance teams to translate business requirements into technical specifications and automated solutions.\n Technical Leadership: Provide technical guidance on the intersection of quantitative modeling and systems engineering, mentoring junior members and influencing the technical roadmap for CORPIO.\n \n You have:  \n \n Bachelor's degree in a quantitative field (e.g. Statistics, Mathematics, Physics) or equivalent practical experience.\n 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience\n Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models\n Strong background in quantitative methods, such as optimization, statistical modeling, or time-series analysis.\n Demonstrated knowledge of Python/SQL/R data analysis libraries and packages\n \n We prefer: \n \n PhD or Master's degree in a quantitative field \n Experience in Capacity Engineering or Infrastructure Optimization at scale.\n Familiarity with ML-driven forecasting and optimization techniques.\n Experience with financial modeling or cost-benefit analysis of technical infrastructure.\n Experience building automation tools for resource management and quota allocation.\n Knowledge of simulation workloads or high-performance computing (HPC) environments.\n \n  \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","autonomous-vehicles","data-pipeline","data-science"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8163689","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-31T16:35:16Z","expires_at":"2026-11-13T16:55:55.008115Z","created_at":"2026-09-01T13:35:18.58398Z","updated_at":"2026-10-14T16:55:55.014008Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/61180fc0-0978-4bdb-ba3e-773344379c4b"}],"page":1,"per_page":20,"total":366,"total_is_exact":true,"total_pages":19}